Geopersia 10 (1), 2020, PP. 89-99 DOI: 10.22059/GEOPE.2019.279698.648474 Detection of Main Rock Type for Rare Earth Elements (REEs) Mineralization Using Staged Factor and Fractal Analysis in Gazestan Iron-Apatite Deposit, Central Iran Fatemeh Soltani 1 , Parviz Moarefvand 1 *, Firouz Alinia 1 , Peyman Afzal 2 1 Department of Mining and Metallurgical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran 2 Department of Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran *Corresponding author, e–mail: [email protected](received: 22/04/2019 ; accepted: 30/06/2019) Abstract Gazestan magnetite-apatite deposit is located in Central Iran and Bafq region, which has been occurred in form of veins, veinlets, and small apatite lenses as well as magnetite in metasomatic rock types such as green chlorite-actinolite rock units. These rocks are situated in the carbonate-volcanic complex of Upper Precambrian-Lower Cambrian Rizo formation. In this study, staged factor analysis and Concentration-Number (C-N) fractal model were used based on core samples for determination of main rock type for Rare Earth Elements (REEs) mineralization. Hence, after normalizing the data by staged factor analysis, the target factors were determined and the factorial map was generated using the C-N fractal modeling. The results showed that the first factor of the sixth step (F1-6) is contained of the REEs with phosphorous. Afterwards, results obtained by the C-N fractal method on the F1-6 indicated that there are five populations for REEs which are compared with different lithological units by evaluation matrix. The evaluation matrix confirmed the compliance of magnetite-apatite units with the high values of mineralization factor. The REEs were accumulated in magnetite-apatite units based on highest Overall Accuracy (OA). Keywords: Rare Earth Elements (Rees), Staged Factor Analysis, Concentration-Number (C-N) Fractal Model, Gazestan, Iron-Apatite Deposit. Introduction Metasomatic iron ores are considered significant economically since they are large and major producers of REEs (Laznicka, 2005). Therefore, exploration of the REEs mineralization in metasomatic iron ores has been attended as an exploration priority in recent years. Furthermore, the rising cost of these valuable elements increasingly has led to the recent discoveries of their deposits in developed countries (Sadeghi et al., 2013; Sarparandeh et al., 2017). The REEs have a variety of applications in modern technology and provide many vital materials related to industry (Humphries, 2011). These elements are classified into light REEs (LREEs) including Ce, Eu, La, Nd, Pr, Sm, and Pm and heavy REEs (HREEs) consisting of Tm, Dy, Er, Gd, Ho, Lu, Tb, Yb, Sc, and Y (Jha, 2014; Simandl, 2014; Emsbo et al., 2015). Recent studies on exploration of the REEs indicate a movement towards using mathematical and statistical modeling methods such as multivariate analysis (Petrosino et al., 2013; Sadeghi et al., 2013; Hellman and Duncan, 2014; Mikhailova et al., 2016; Rahimi et al., 2016; Zaremotlagh & Hezarkhani, 2016). Several modeling methods have been used in different deposits based on mathematical and statistical methods. However, classical statistical parameters including mean, standard deviation, and histogram and box plot have been employed to determine different geochemical populations (Davis, 1976; Hawkes & Webb, 1979; Afzal, 2010; Yasrebi et al., 2013; Mokhtari et al., 2014, Ghezelbash & Maghsoudi, 2018). Factor analysis has been used widely as one of the multivariate analysis methods to interpret geochemical data. The major purpose of this analysis is to justify the greatest variability between the observations and variables through linear combination of multiple variables in a space with lower dimensions (Krumbein & Graybill, 1965; Tripathi, 1979; Johnson & Wichern, 2002; Afzal et al., 2016). A large part of variability can be justified by a limited number of new variables, and the coefficients of the initial variables included for calculating each of the principal components may be calculated by either a covariance or correlation matrix (Reimann et al., 2002). In the results obtained by factor analysis, where either correlation or covariance matrix is used, one observes the emergence of certain noise elements and the absence of mineralization indicator
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Geopersia 10 (1), 2020, PP. 89-99 DOI: 10.22059/GEOPE.2019.279698.648474
Detection of Main Rock Type for Rare Earth Elements (REEs) Mineralization Using Staged Factor and Fractal Analysis in Gazestan
Iron-Apatite Deposit, Central Iran
Fatemeh Soltani1, Parviz Moarefvand1*, Firouz Alinia1, Peyman Afzal2 1 Department of Mining and Metallurgical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran 2 Department of Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran *Corresponding author, e–mail: [email protected]
(received: 22/04/2019 ; accepted: 30/06/2019)
Abstract Gazestan magnetite-apatite deposit is located in Central Iran and Bafq region, which has been occurred in form of veins, veinlets, and small apatite lenses as well as magnetite in metasomatic rock types such as green chlorite-actinolite rock units. These rocks are situated in the carbonate-volcanic complex of Upper Precambrian-Lower Cambrian Rizo formation. In this study, staged factor analysis and Concentration-Number (C-N) fractal model were used based on core samples for determination of main rock type for Rare Earth Elements (REEs) mineralization. Hence, after normalizing the data by staged factor analysis, the target factors were determined and the factorial map was generated using the C-N fractal modeling. The results showed that the first factor of the sixth step (F1-6) is contained of the REEs with phosphorous. Afterwards, results obtained by the C-N fractal method on the F1-6 indicated that there are five populations for REEs which are compared with different lithological units by evaluation matrix. The evaluation matrix confirmed the compliance of magnetite-apatite units with the high values of mineralization factor. The REEs were accumulated in magnetite-apatite units based on highest Overall Accuracy (OA).
Introduction Metasomatic iron ores are considered significant economically since they are large and major producers of REEs (Laznicka, 2005). Therefore, exploration of the REEs mineralization in metasomatic iron ores has been attended as an exploration priority in recent years. Furthermore, the rising cost of these valuable elements increasingly has led to the recent discoveries of their deposits in developed countries (Sadeghi et al., 2013; Sarparandeh et al., 2017).
The REEs have a variety of applications in modern technology and provide many vital materials related to industry (Humphries, 2011). These elements are classified into light REEs (LREEs) including Ce, Eu, La, Nd, Pr, Sm, and Pm and heavy REEs (HREEs) consisting of Tm, Dy, Er, Gd, Ho, Lu, Tb, Yb, Sc, and Y (Jha, 2014; Simandl, 2014; Emsbo et al., 2015). Recent studies on exploration of the REEs indicate a movement towards using mathematical and statistical modeling methods such as multivariate analysis (Petrosino et al., 2013; Sadeghi et al., 2013; Hellman and Duncan, 2014; Mikhailova et al., 2016; Rahimi et al., 2016; Zaremotlagh & Hezarkhani, 2016). Several modeling methods have been used in
different deposits based on mathematical and statistical methods. However, classical statistical parameters including mean, standard deviation, and histogram and box plot have been employed to determine different geochemical populations (Davis, 1976; Hawkes & Webb, 1979; Afzal, 2010; Yasrebi et al., 2013; Mokhtari et al., 2014, Ghezelbash & Maghsoudi, 2018).
Factor analysis has been used widely as one of the multivariate analysis methods to interpret geochemical data. The major purpose of this analysis is to justify the greatest variability between the observations and variables through linear combination of multiple variables in a space with lower dimensions (Krumbein & Graybill, 1965; Tripathi, 1979; Johnson & Wichern, 2002; Afzal et al., 2016). A large part of variability can be justified by a limited number of new variables, and the coefficients of the initial variables included for calculating each of the principal components may be calculated by either a covariance or correlation matrix (Reimann et al., 2002).
In the results obtained by factor analysis, where either correlation or covariance matrix is used, one observes the emergence of certain noise elements and the absence of mineralization indicator
90 Soltani et al. Geopersia, 10 (1), 2020
elements in one factor. Thus, in order to improve the results and modify the effect of noise elements, staged factor analysis has been proposed in a study conducted by Yousefi et al., (2012).
Fractal modeling has been used to separate various geochemical populations as well as barren and mineralized rocks. In this regard, Number-Size (N-S) fractal model was introduced by Mandelbrot (Mandelbrot, 1983); power Spectrum-Area (S-A) technique was presented by Cheng et al., (1994, 2000); Concentration-Distance (C-D) model was proposed by Li et al., (2003); Concentration-Volume (C-V) methodology was suggested by Afzal et al., (2011); power Spectrum-Volume (S-V) fractal method was introduced by Afzal et al., (2012), and Concentration-Number (C-N) model was proposed by Hassanpour & Afzal (2013).
In the present study, a relationship was found between the lithological units and the concentration of REEs in Gazestan iron-apatite deposit (Central Iran) using a combination of stage factor analysis and the C-N fractal model. The correlation was carried out between results and rock types by evaluation matrix.
Methodology Staged factor analysis Multivariate statistical approaches explore the relationships between multiple variables simultaneously. These multivariate methods are often employed to reduce the multivariate dataset so that one can interpret the trend and variability in the dataset using the reduced data (Chandrajith et al., 2001; Helvoort et al., 2005; Gholami et al., 2012).
The staged factor analysis is a multivariate method (Yousefi et al., 2014). This can be used for extracting significant multi-element anomalous signatures. In this approach, In order to find multi- element associations in a geochemical dataset, non-indicator (noisy) elements are identified progressively and are excluded from the analysis until a satisfactory significant multi-element signature is obtained (Yousefi et al., 2012). In order to perform FA, classical PCA (non-robust) was used for extracting the common factors. Subsequently, the Varimax method was used for rotation and factors with eigenvalues of >1 were retained for interpretation (Kaiser 1958). In addition, the threshold value of 0.6 for loadings was considered to extract the significant multi-element geochemical signature of the deposit-type sought.
In the first stage, factor analysis was performed
on the initial data; thereafter, with respect to the considered threshold limit, if there was any element that did not participate in any of the factors, it was deleted and factor analysis would be continued on the data of the remaining elements until all noise elements were eliminated so that there would be no element which did not fit into any of the factors considering the threshold limit. These factors are called clean factors.
In the second stage, those factors having indicator elements were selected for desirable mineralization, and the phases mentioned in the first stage were performed on the elements of these factors. Factor advantages obtained in the last step were utilized to perform exploratory operations (Yousefi et al., 2012; Afzal et al., 2016).
Concentration–Number (C–N) fractal Modeling Fractal methods can explain the relationships between the results obtained in geological, geochemical, structural, and mineralogical studies. Correspondingly, logarithmic graphs are regarded as tools for isolating and separating geochemical communities in geochemical information. After plotting these logarithmic graphs, wherever the gradient of the curve experiences a sharp change, it means that the geological and mineralogical community has been transformed.
The C–N fractal model was used to define the geochemical background and anomaly threshold values (Mandelbrot, 1983; Deng et al., 2010; Hassanpour & Afzal, 2013). The model has the general form as follow: N(≥ρ) ∞ ρ –β (1) Where N (≥ρ) represents the sample number with concentration values greater than the ρ value. ρ and β are the concentration of ore element and fractal dimension, respectively. In this method, geochemical data has not undergone pre-treatment and evaluation (Deng et al., 2010; Hassanpour & Afzal, 2013; Afzal et al., 2017).
Evaluation matrix In order to investigate the correlation of the zones obtained from the fractal method and geological observations, the authors used evaluation matrix. This matrix was proposed first by Caranza (2011), to identify the gold anomalies of stream sediments located in the northwest of Philippines. Using this matrix, the results obtained by the C-N fractal model and geological observations are compared with each other by considering the matrix’s
Detection of Main Rock Type for Rare Earth Elements (REEs) Mineralization … 91
components. Following the calculations of the related matrix, any data with the greatest overall accuracy can be considered as the definitive result with the least error rate. Geological setting and mineralization Gazestan deposit is located in Yazd province, about 78 km from the city of Bafq and 10 km from the SE of Gazestan village. According to structural parameters, this deposit belongs to Central Iran and also Bafq-Posht-e Badam metallogenic zone (Afzali et al., 2012). The rock types in this area are related to the Rizo series and are consisted of carbonate rocks, shale, tuff, sandstone, and volcanic rocks.
Additionally, intrusions in the form of stocks and dikes with mid-to-basic combinations are occurred in the deposit. The basic dikes are composed of diorite-gabbro and diabase principally.
Green/metasomatic rocks with acidic compositions, which are appeared in green due to metasomatism, are known as the host of iron and phosphate mineralization (Dehghanzade-Bafghi et al., 2017). Metasomatic processes have taken place simultaneously or slightly ahead of mineralization. The intensity of metasomatic processes increases as one approaches the mineralization zone, Fig.1. (Parsi Kan Kav Company 2015).
Figure 1. Geological map, Gazestan deposit (modified from Sepehrirad et al., 2018), borehole locations are mapped (up) and perspective boreholes locations (down), gray boreholes are not assayed.
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Generally, the dominant composition of the rocks in this region is acidic volcanic and microgranite. Except for diorite-gabbro rocks found in the western part of the area, which is considered as a basic mass, as well as some dikes and small stocks. Therefore, the formation of acidic rocks in this deposit is considered to be originated in granitic magma (Afzali et al., 2012).
The Gazestan deposit is occurred in the vein form, veinlet and small apatite lenses with magnetite in a green chlorite-actinolite rock unit in the carbonate-volcanic complex of Rizo Formation (Dehganzadeh Bafghi et al., 2019). Moreover, it dates back to the Upper Precambrian- Lower Cambrian periods (Afzali et al., 2012).
The green rock unit which includes intrusive volcanic rocks is consisted of andesite, micro-diorite, tuff, as well as mafic-ultramafic rocks which have been altered largely. It seems that veins, magnetite, and apatite lenses have been concentrated in this unit as a pure phase. The apatite mineral is contained of fluorine, and REEs found in this mineral are micro-monazite inclusions and other minerals (Afzali et al., 2012; Parsi Kan Kav Company 2015).
In Gazestan deposit, the mineral is a mixture of magnetite-apatite in various ratios, typically accompanied by quartz and calcite. Quartz and calcite have been formed in the late stages following mineralization. The mineralization zone in Gazestan reaches over 2.4 km long and over 0.7 km wide (Afzali et al., 2012; Parsi Kan Kav Company 2015).
Metasomatism is more evident in volcanic rocks, and the rocks hosting mineralization display sharp metasomatism. The observed metasomatism in this area is mostly occurred in the form of silicic, chloritic, actinolite and argillic types, which are associated with mineralization and have the most development in the area (Dehganzadeh-Bafghi et al., 2019). However, sericitic, potassic, tourmalinic and epidotic metasomatism have also been formed in rock units (Mokhtari, 2015; Sepehrirad et al., 2018).
Diffusion in the study area can be classified into at least 5 groups of faults with the east-west trend, north-south trend, northeast-southwest trend, northwest-southeast trend, and thrust faults. The faults with the east-west trend are the oldest fractures in the region because displacements and variation in dip and strike of the fault that have
been made by other faulting (newer ones) systems. The faults that are in South of studied area, placed volcanic rocks close to the carbonate rocks. Thrust faults have been displaced by the northeastern-southwestern faults (Madani-Esfahani & Asghari, 2013). One of the main functions of these faults is that they have placed hard-eroding rocks such as carbonates and sandstones on soft-eroding units, which are mineral units. Thereby, these faults have prevented further mineral erosion (Parsi Kan Kav Company, 2015). Dataset In the present study, 908 core samples were logged from 12 exploratory boreholes with a total length of 1814 meters. The obtained samples are two meters length. A total of 56 elements including LREEs, HREEs, and total REEs were analyzed by the ACME CEMEX Company using ICP for grade assaying and XRD, and XRF for petrological studies.
In order to determine the distribution of REEs in the study area, statistical parameters including mean, median, variance, histogram, and box plot have been calculated separately in different rocks. Using the data of area, the frequency diagram of LREEs, HREEs and total REEs have been generated so that the target rock unit could be determined for modeling. Thus, using classical statistics, the number of rock units in the region (17) were merged based on the abundance of major minerals and rock units, into seven rock types, as shown in Tables 1 and 2, respectively.
After combining the rock units, the scatter diagram was drawn regarding the ratio of heavy-to-light REEs. Therefore, all the units of rhyodacite, andesite-rhyodacite, and tuff-rhyodacite were converted into rhyodacite. Moreover, all the tuff-andesite units in tuff units, magnetite-albite units, andesite- magnetite, magnetite-diorite, and magnetite-tuff were converted into magnetite unit. Finally, dacite and andesite were combined into dacite.
In order to show the distribution of the concentration values of REEs, a boxplot was drawn for the combined rock units, as shown in (Fig. 2). It is evident that magnetite-apatite units are of the highest value of REEs with a mean of 1362 ppm, and the units of rhyodacite, dacite, and waste rocks have a mean which is equal to 569 ppm, 341 ppm and 380 ppm, respectively.
Detection of Main Rock Type for Rare Earth Elements (REEs) Mineralization … 93
Table 1. Calculation of statistical parameters in the rock units of Gazestan region (the order of rocks: andesite, dacite, dacite-andesite, diorite, diabase, magnetite, magnetite-albite, magnetite-andesite, magnetite-diorite, magnetite-tuff, slag, rhyodacite, andesite- rhyodacite, tuff-rhyodacite, tuff, andesite-tuff, and waste rock).
Rock Type Total Count
Mean of HREE
Median of HREE
Mean of LREE
Median of LREE
AND 216 208 174.6 619.4 496.4
DAC 64 109.45 104.81 232.5 209.8
DACAN 2 159.16 159.16 574.19 574.19
DIO 7 280 191.4 1093 434
DYA 2 54.8 54.800 151.52 151.2
MAG 34 325.7 307.9 1154 988
MAGALB 7 227.3 243.4 925 992
MAGAN 104 258.6 243 1041.7 952
MAGDI 1 200.2 200.2 878.03 878.03
MAGTUF 98 298.9 249.5 1107 796
OVER 2 599 599 539 539
RYD 12 112.6 98 312.2 210.8
RYDAN 3 203.3 134.8 789 651
RYDTU 2 168.6 168.6 629 629
TUF 160 200.9 158.2 632.1 460.8
TUFAN 186 239.6 174.4 795.1 526.5
WASTE 8 158.7 139.3 228 229.3
Grand Total 908 3804.81 11700.74
Table 2. Calculation of statistical parameters after merging rock units.
Figure 2. Boxplot of the total REEs in the different rock units.
94 Soltani et al. Geopersia, 10 (1), 2020
Discussion The ratio of the distribution of LREEs/HREEs is not the same in rock units. Considering the high variation in the values of variables, logarithm was taken at the base 10 for each of the concentration values of light and heavy REEs, in order to delimit the value range of the axes in the coordinate system, as shown in Fig. 3 and to magnify the variations.
As illustrated in Fig. 2, magnetite units, while having a total average concentration of greater than other units; show a great ratio of light-to-heavy elements since they are located mostly at the top of the one-to-one correspondence line. The ratio of light-to-heavy REEs is equal to 3.8 in magnetite units, and in diorite units, which the number of its samples is quite small, it is equal to 3.9. Moreover, dacite appearing in red are in the lower parts of the diagram, and the ratio of heavy-to-light elements is remarkably high.
The ratio of LREEs/HREEs in rhyodacite, andesite, dacite, tuff and waste rocks are equal to 3.1, 3, 2.1, 3.2 and 1.2, respectively. In other words, this ratio increases significantly with the increase in the total average concentration value in each rock unit.
Figure 3. Diagram of distribution of rock units in scatter plot of LREE and HREE
In the current study, in order to extract the main components of mineralization, staged factor analysis was used. Prior to the rotations of this stage, using Normal Score Transformation, all data were converted into normal distribution with the mean of 0 and variance of 1. In the first step, taking into account the threshold limit of 0.6 on the correlation values and the 13 factors, noise elements were deletedwith values lower than the threshold limit of 0.6. In other words, the elements whose values were not higher than the threshold limit were removed from all of these factors. Specifically, in
this step, the elements of As, Ba, Bi, Co, Ga, Ni, Sr, W, and Zn were removed as noise or uncorrelated elements.
In the second step of factor analysis, 9 factors were obtained. Here, the noise elements were removed again and the elements such as Cu, In, Mo, Pb, Sb, and Ta were eliminated and 8 factors were obtained. In the next step, the Cr and Tl elements were removed and 7 factors were obtained. In this step, i.e. the fifth step, the Be element was deleted only and the number of factors did not change. No noise element was found in this step. In other words, there is no element that cannot be classified in any of the factors considering the threshold limit of 0.6. Thus, the first phase of the factor analysis was completed and the factors obtained were clean.
In the second phase, we had to choose factors containing the indicator elements of the desirable mineralization. Regarding the study area, considering that the objective is to investigate and identify REEs in the magnetite-apatite rock units in the region, the factors of 1.2 and 3 were considered as the target factors because they were contained of desirable elements, i.e. Fe and REEs. Elements with a threshold lower than 0.6 were removed in this stage. These elements were Ag, Ca, Cd, Cs, Li, Mg, Mn, Nb, and Ti (Table 3). Thus, three factors were obtained in the sixth step, and all the elements (Table 4) had higher values than the threshold limit. All the remaining elements in the three factors had a minimum correlation of 0.6 with their corresponding factor (Table 4). Therefore, using factor scores, the C-N fractal model was carried out on the first factor of step 6 (F1-6) which had multi-fractal behaviors. The model was used to identify and distinguish REEs in magnetite-apatite rock units in the Gazestan deposit.
In addition, the C-N log-log plot was generated (Fig. 4). The fractal diagram presented in Fig. 4 illustrates the breakpoints between the straight lines in the diagram of values as well as the threshold in order to distinguish rock units and various geological communities.
These breaks are corresponded with 2.77, 1.42, 0.60, and 0.163, respectively. In the last step, there is a distinguished multi-fractal behavior which represents a phase and an enriched rock unit. Since in the Gazestan deposit, the mineral is a mixture of magnetite and apatite, this full-concentration part can be considered to be corresponded with magnetite-apatite rock units.
Detection of Main Rock Type for Rare Earth Elements (REEs) Mineralization … 95
Table 3. Rotated factor matrix for the fifth stage of the staged factor analysis.
Table 4. Rotated factor matrix for the sixth stage of the staged factor analysis.
Figure 4. Logarithmic curve of concentration–number of the scores of the first factor in the sixth step of staged factor analysis
96 Soltani et al. Geopersia, 10 (1), 2020
Using the evaluation matrix (Table 5), the present study attempted to investigate the correspondence of the Magnetite-Apatite zone obtained from the concentration-number fractal method and geological observations. Using this matrix, the results achieved from the multi-fractal model will be compared with each other by considering the matrix’s components.
After calculating the desired matrix, any datum that has the greatest overlap with the results of the concentration-number fractal model will have the highest overall accuracy and can be seen as a definitive result with the lowest error rate. The matrix was also provided for Andesite and tuff
rocks. Comparing the results of andesite and tuff with those of Magnetite-Apatite illustrates that the errors are significantly higher and, as expected it confirms that the desired range of high REE values has compliance mostly with Magnetite-Apatite. Conclusion The main mineralization is occurred in magnetite-apatite units in the Gazestan deposit. However, according to statistical studies conducted in the present research, there are also significant full-concentration quantities of rare elements in other units of this deposit such as tuff and andesitic unit.
Table5. Overall accuracy, Type 1 and Type 2 errors calculated to match the first factor model of the sixth step with the geological model (magnetite-apatite)
Geological Model
Outside the zone(magnetite-apatite)
Within the zone(magnetite-apatite)
False positive(B)= 51 True positive(A)= 26 Within the zone1.42037-
3.47384 Fractal model
True negative(D) = 612 False negative (C) = 218 Outside the zone 1.42037-3.47384
Error type2 = 0.07 Error type1=0.893
overall accuracy = 70%
Table 6. Overall accuracy, Type 1 and Type 2 errors calculated to match the first factor model of the sixth step with the geological model (Andesite)
Geological Model
Outside the zone(Andesite )
Within the zone(Andesite )
False positive(B)=67 True positive(A)= 10 Within the zone1.42037-3.47384 Fractal
model True negative(D) = 622 False negative (C) = 208 Outside the zone 1.42037-
3.47384
Error type2 = 0.1 Error type1=0.95
overall accuracy =69%
Table 7. Overall accuracy, Type 1 and Type 2 errors calculated to match the first factor model of the sixth step with the geological model (Tuff)
Geological Model
Outside the zone (Tuff ) Within the zone (Tuff )
False positive(B)= 37 True positive(A)= 40 Within the
zone1.42037-3.47384 Fractal model
True negative(D) = 525 False negative (C) = 305 Outside the zone 1.42037-3.47384
Error type2 = 0.06 Error type1 = 0.88
overall accuracy = 62 %
Detection of Main Rock Type for Rare Earth Elements (REEs) Mineralization … 97
Based on the results, with the increase in the concentration of rock units, the ratio of the concentration of light to heavy REEs increases significantly. In other words, the share of light elements in apatite-magnetite units is far more than those of rhyodocite and dacite, which have low levels of REEs.
Based on multivariate statistical studies, using staged factor analysis demonstrated desirable efficiency in separating elements irrelevant (noise) to mineralization. This suggestion becomes more important dealing with a large number of similar variables, in which was the case of the present study (56 concentration variables), that would disrupt the multivariate identification system where they are used all at once. Applying this technique, inefficient
elements in the process of separation were removed practically from the dataset. Consequently, having refined information, more accurate separation of rock communities was carried out on the factor obtained from the new data based on the concentration-number fractal model.
Using a verification matrix, this separation led to a quantitative and modeled distinction between magnetite-apatite units as the main component of mineralization in Gazestan. Acknowledgment This study was carried out using the data provided by Parsi Kan Kav Engineering Company. The authors are grateful to all the personnel of this company.
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